corl-skills

Plugin corl-skills from marketplace awesome-journal-skills in brycewang-stanford/Awesome-Journal-Skills. Commands read from the repo's manifest — 1 month ago.

Skills (12)

• corl-artifact-evaluation

  Use when packaging the artifacts of a CoRL paper — code, training configs, demonstration datasets, policy checkpoints, simulation environments, and benchmark definitions — as anonymous review-time evidence and as durable public releases after acceptance, in a venue with no formal artifact-badging tr

• corl-author-response

  Use when drafting the CoRL rebuttal — a single-page PDF due days after reviews are released, aimed at reviewers, the Area Chair, and the discussion window that follows. Covers triage against the first-round gate, one-page layout economy, presenting new numbers compactly, and tone for an exchange tha

• corl-camera-ready

  Use when converting an accepted CoRL paper into its final form — the 9-page camera-ready main text with the extra page for review feedback, the final-mode corl LaTeX template, appendix merged into one PDF, de-anonymization, external hosting for videos because PMLR accepts none, and the October deadl

• corl-experiments

  Use when designing or auditing experiments for a CoRL robot-learning paper — seeds and evaluation-episode counts, task-suite breadth, real-robot versus simulation evidence, sim-to-real gap measurement, baseline fairness across BC/RL/VLA families, generalization splits, and statistics for success-rat

• corl-related-work

  Use when positioning a CoRL paper against the literature — the robot-learning lineage across CoRL/RSS/ICRA, the ML-methods stream from NeurIPS/ICLR/ICML, the fast-moving VLA and foundation-model wave, classical robotics baselines, concurrent arXiv work, and PMLR citation hygiene including the year-o

• corl-reproducibility

  Use when making a CoRL robot-learning paper reproducible — pinning simulator and driver versions, releasing training configs, demonstration data and checkpoints, documenting hardware setups that cannot be rerun, seed policy, evaluation scripts, and honest availability statements for code, data, and

• corl-review-process

  Use when reasoning about how CoRL reviews a paper — the OpenReview double-anonymous pipeline, reviewer/AC/SAC hierarchy, rubric scoring with weak accept as the acceptance threshold, the first-round rejection gate before rebuttal, the reviewer-AC discussion window, decision meetings, and public revie

• corl-submission

  Use when preparing or auditing a CoRL OpenReview submission — the corl_2026 LaTeX template, the 8-page main text with a mandatory Limitations section counted inside it, uncounted references and appendix, the supplementary file and 250 MB video, double-anonymous rules, abstract registration, and dual

• corl-supplementary

  Use when assembling CoRL supplementary material — the strongly encouraged overview video under the 250 MB cap and roughly three minutes, the optional appendix inside the same PDF that reviewers need not read, code and data attachments, and anonymization of everything, with the PMLR no-video rule sha

• corl-topic-selection

  Use when deciding whether a project belongs at CoRL, the Conference on Robot Learning, or should be routed to ICRA, IROS, RSS, NeurIPS, ICLR, ICML, or a journal — based on whether the learned component is the contribution, what embodied evidence exists, and which reviewer community should judge the

• corl-workflow

  Use when planning a CoRL submission cycle end to end — the late-May abstract and paper deadlines, the summer review window, first-round rejection risk, the August one-page rebuttal and discussion period, autumn decisions, the October PMLR camera-ready, and the November conference — including what to

• corl-writing-style

  Use when writing or revising a CoRL paper's prose — leading with the embodied task and the learned component, calibrating claims to evaluation scale, writing the mandatory Limitations section as a scored asset, fitting the argument into 8 pages, and satisfying a dual reviewer audience of ML and robo

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